Michael 5daae9e5fa test(footer): move marker out of footer into main content flow
User confirmed the previous marker landed inside the Help/Close
sticky footer — which is NOT the offending white bar. They want the
sticky footer kept; the white strip they're trying to remove sits
*above* the footer in the main content area.

Move the marker out of ``#datatools-sticky-footer`` and render it
via ``st.markdown`` immediately before the ``st.iframe`` call that
injects the footer. That places it at the very bottom of
``stAppViewBlockContainer`` — exactly where the iframe wrapper
(``stElementContainer``) and the block container's
``padding-bottom: 3rem`` reservation live.

Styled as a red dashed banner so it's unmistakable. If it lines up
with the white strip clipping text on scroll, one of those two is
the culprit and the next commit can target it.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-18 22:09:21 +00:00

🌐 Language: English · Español

DataTools

Local CSV / Excel cleaning. CLI + browser GUI, no cloud, no install ceremony. GUI ships with English and Spanish language packs.

Tools

# Tool Status
01 Find Duplicates — exact + fuzzy match, 5 normalizers, survivor rules, audit Ready
02 Clean Text — whitespace, smart chars, BOM, line endings, case ops Ready
03 Standardize Formats — dates, phones, emails, addresses, names, currencies, booleans Ready
04 Fix Missing Values — disguised-null detection, profile, mean/median/mode/ffill/bfill/interpolate, drop strategies Ready
05 Map Columns — fuzzy auto-rename, target schema with type coercion, required fields with defaults, drop/reorder Ready
06 Find Unusual Values Coming Soon
07 Combine Files Coming Soon
08 Quality Check Coming Soon
09 Automated Workflows — chain tools with recommended (not forced) order, save/load JSON, automate weekly cleanups Ready

Download (non-technical users)

Pre-built installers — no Python required:

Platform Download First-launch note
macOS DataTools-X.Y.Z-mac.dmg Drag DataTools.app into /Applications, then double-click.
Windows DataTools-X.Y.Z-win-setup.exe Run the installer; launches from Start Menu.
Linux DataTools-X.Y.Z-linux-x86_64.AppImage chmod +x the file, then double-click.

Latest release: see GitHub Releases (or the Gumroad listing). The installers are ~150200 MB; the launcher boots a local server at http://127.0.0.1:8501 and opens your browser. Nothing is sent to the cloud.

Install from source (developers)

pip install -r requirements.txt

Python 3.10+ required.

Run

GUI (recommended):

streamlit run src/gui/app.py

CLI — seven entry points:

python -m src.cli            customers.csv [--apply]   # dedup
python -m src.cli_text_clean messy.csv     [--apply]   # text clean
python -m src.cli_format     intl.csv      [--apply]   # format standardize (auto-streams >100 MB)
python -m src.cli_missing    holes.csv     [--apply]   # missing values
python -m src.cli_column_map vendor.csv    [--apply]   # column mapper
python -m src.cli_pipeline   any_file.csv  [--apply]   # chain tools end-to-end
python -m src.cli_analyze    any_file.csv  [--json]    # scan only

Every CLI runs preview-only by default; add --apply to write output.

Language

The GUI sidebar has a language picker. Packs ship for English and Español (src/i18n/packs/); the choice persists for the session. Adding a language: drop a <code>.json next to en.json mirroring its key tree, then list it in LANGUAGES. See Developer Guide §i18n.

Review & Normalize gate

Every uploaded file passes through a CSV-normalization gate before any tool sees it. The analyzer flags ~15 issue types (whitespace, NBSP / zero-width chars, BOM, encoding, smart punct, dirty headers, null sentinels, mojibake, …) tagged by confidence (high / medium / low) and fix action. The GUI shows each finding with Auto-fix / Skip / Customize, a live before/after preview, and an encoding-override picker. Tool pages refuse to load until the gate passes.

Output

Every run writes:

  • {input}_<tool>.csv — the cleaned data
  • {input}_changes.csv (text cleaner) or {input}_match_groups.csv (dedup) — audit trail
  • logs/<tool>_YYYYMMDD_HHMMSS.log — debug-level run log

Original input file is never modified.

Docs

Dependencies

pandas, openpyxl, rapidfuzz, phonenumbers, typer, loguru, charset-normalizer, streamlit. Optional: ftfy for mojibake repair.

License

Proprietary.

Description
Data tools development
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